Nuclear Medicine

Radiomics-enhanced 18F-AV45 PET/MRI for integrative assessment and centiloid estimation of amyloid-β burden in Alzheimer's disease.

OBJECTIVE: Reliable assessment of cerebral amyloid-β (Aβ) deposition is essential for the diagnosis and management of Alzheimer's disease (AD). This study aimed to evaluate the feasibility of integrating radiomics-enhanced 18F-florbetapir positron emission tomography/magnetic resonance imaging (18F-AV45 PET/MRI) features for Aβ status evaluation and to further explore their potential for continuou...

Evaluation of Baseline and Interim-Therapy PET Features for Prognostication in High-Risk Pediatric Hodgkin Lymphoma: A Retrospective Analysis of the AHOD1331 Trial.

This study aimed to evaluate the prognostic value of conventional and advanced PET metrics for predicting progression-free survival in high-risk pediatric Hodgkin lymphoma (HL), using data from the Children's Oncology Group AHOD1331 trial. Methods: This retrospective analysis included 558 patients from 150 institutions. Conventional PET metrics and radiomics features were extracted from both basel...

Revisiting Perfusion in the Forgotten Right Ventricle: Artificial Intelligence-Enhanced Quantification on PET/CT.

Increased right ventricular (RV) radiotracer uptake on perfusion imaging has been recognized as a marker of increased cardiovascular risk. However, th...

End-to-End PET/CT Interpretation and Quantification with an LLM-Orchestrated AI Agent: A Real-World Pilot Study.

Although deep learning models have improved individual PET analysis, image processing, and quantification tasks, end-to-end automation from raw DICOM ...

Deep integration of clinical metadata with [18F]FDG PET/CT imaging for histological subtyping in non-small cell lung cancer: a multi-center study.

PURPOSE: To develop and validate a multimodal deep learning framework that integrates clinical metadata with [18F]FDG PET/CT imaging to resolve overla...

Contrast injection protocols used in oncological CT and opportunities for practice optimisation.

INTRODUCTION: Contrast-enhanced CT is central to oncological imaging, yet no official guidelines exist for contrast injection protocols. As a result, ...

A system-level DOI discrimination method based on SSDA for a brain-dedicated DOI-PET scanner.

OBJECTIVE: Depth-of-interaction (DOI) information is essential for improving the spatial resolution in positron emission tomography (PET) imaging. Exi...

Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.

Learning-based image classification has become central to modern medical imaging, but the field is changing rapidly: foundation models, vision-languag...

Investigating AD and non-AD [18F]flortaucipir distribution patterns in a memory clinic cohort.

PURPOSE: Amyloid (A) deposition represents a specific pathological hallmark of Alzheimer's disease (AD). Clinical diagnostic protocols frequently rely...

A deep learning framework for lesion-level treatment response prediction in hodgkin lymphoma using PET/CT tensor radiomics.

BACKGROUND: Accurate prediction of treatment response in Hodgkin lymphoma (HL) is crucial for personalized therapy. The Tensor Radiomics (TR) paradigm...

Multimodal multitask deep learning for grading management system in non-small cell lung cancer.

Accurate histologic subtyping, tumor node metastasis classification (TNM) staging and prognostic assessment are central to clinical management of non-...

Investigation of the molecular network underlying PET-MPs-induced inflammatory bowel disease via integrated machine learning and molecular docking approaches.

Polyethylene terephthalate microplastics (PET-MPs), as environmental contaminants, have raised significant concerns due to their potential toxicity, l...

Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: Clinical insights, pitfalls, and observer agreement analyses.

PURPOSE: This study addresses critical gaps in automated lymphoma segmentation from PET/CT imaging, often overlooked in prior work. While deep learnin...

Comparison of ChatGPT and dietitians in formulating diet plans and recommendations for patients with cardiometabolic diseases.

BACKGROUND: Artificial Intelligence (AI) is transforming personalized medicine, yet its efficacy constitutes a dynamic factor in the field of health a...

A methodological framework for integrating generalisable deep learning and radiomics fusion model for early lung cancer detection across multi-centre imaging datasets.

Early detection of lung cancer remains one of the most effective strategies for improving survival; however, diagnostic performance is limited by vari...

Functional Imaging in Hereditary Endocrine Neoplasms: Evolving Modalities and Clinical Implications.

Hereditary endocrine neoplastic syndromes require structured, lifelong surveillance owing to their multisystem involvement, variable penetrance, and h...

Spinal cord FDG PET uptake in healthy adults: an AI-driven segmentation study.

PURPOSE: 18F-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET)/Computerized Tomography (CT) is an important imaging modality in oncology, bu...

Swin UNETR-based prediction of [68Ga]Ga-FAPI-46 PET/CT dose rate maps in cancer patients: quantitative comparison with [18F]FDG PET/CT.

BACKGROUND: Fibroblast activation protein (FAP) is a promising theranostic target due to its high stroma expression in numerous malignancies. This stu...

QSM vs dopamine transporter PET: χ+ Mapping Shows Stronger Longitudinal Associations with Nigral Degeneration than Conventional QSM in Parkinson's Disease.

BACKGROUND: Iron accumulation in the substantia nigra (SN) is a hallmark of Parkinson's disease (PD). Quantitative susceptibility mapping (QSM) includ...

Hybrid-CMLP: Hybrid CNN-MLP Networks for Low-to-standard-dose PET Synthesis.

Positron Emission Tomography (PET) is a critical modality in medical imaging for detecting abnormalities and diagnosing diseases. However, the radiati...